Data-Driven Optimal Output Feedback Control of Unknown System Model via Adaptive Dynamic Programming

This paper investigates the linear quadratic optimal output feedback control problem for an unknown linear continuous-time system. Combined with adaptive dynamic programming and optimal control theory, an online data-driven iteration learning algorithm is developed to learn an optimal controller from system data. The main advantage of the proposed algorithm is that it does not require an initial stabilizing control policy, a full-rank condition, or historical data storage to guarantee algorithm convergence. This is fundamentally different from the existing results based on the least-squares method, which requires these conditions. Moreover, the developed algorithm uses only the input and output data of the system, which solves the problem of unmeasurable system states. The simulation results demonstrate the efficacy of the proposed algorithm, and its superiority is demonstrated by comparison with the existing algorithms.